Future work. Our work tries to identify radiogenomic associations between radiomic phenotypes and genomic markers in LGGs by incorporating the biological structure of the tumor into the modeling framework. However, this could be further explored in several directions such as identifying the tumor regions/spherical shells based on known segmentation of the tumor subregions. This could additionally be informed by the shape of the tumor, which the PDFs do not capture, as they only assess the heterogeneity in the tumor voxel values. Also, our model explores linear relationships between the imaging-based PC scores and the gene expression, which could potentially be extended to study nonlinear associations (e.g. using semi-parametric additive models [1]}, [2]}). Our future work will include integrating these radiogenomic findings into predictive models for clinical outcomes such as overall survival, time to progression.
